Gigapixel and larger images are becoming increasingly popular due to the availability of high-resolution cameras and inexpensive robots for the automatic capture of large image collections. These tools simplify the acquisition of large stitched panoramas that are becoming easily accessible over the Internet. Even more massive images from aerial satellite photography are freely distributed, e.g. from the USGS website. At the same time, computed tomography (CT) and microscopy scans allow acquisition of massive three-dimensional (3D) images for mechanical engineering applications or reconstruction of biological structures. The full potential of such imagery may only be realized by scientists or artists by enhancing, manipulating, and/or compositing the original images. However, using the combined panoramas typically requires offline processing to reduce varying lighting and coloring artifacts or to perform other editing or analysis on the data sets because the real time processing of very large volumetric meshes introduces specific algorithmic challenges due to the impossibility of fitting the input data into the main (in-core) memory of a computer. The basic assumption of uniform-constant-time access to each memory location is not valid because part of the data is stored out-of-core or in external memory. The performance of most algorithms does not scale well in the transition from the in-core to the out-of-core processing conditions. The performance degradation is due to the high frequency of input/output operations that may dominate the overall run time. Thus, because of the large data set sizes, enhancing, manipulating, and/or compositing the images or otherwise analyzing the data is computationally expensive.